Citing this article

A standard form of citation of this article is:

Izquierdo, Segismundo S., Izquierdo, Luis R. and Gotts, Nicholas M. (2008). 'Reinforcement Learning Dynamics in Social Dilemmas'. Journal of Artificial Societies and Social Simulation 11(2)1 <http://jasss.soc.surrey.ac.uk/11/2/1.html>.

The following can be copied and pasted into a Bibtex bibliography file, for use with the LaTeX text processor:

@article{izquierdo2008,
title = {Reinforcement Learning Dynamics in Social Dilemmas},
author = {Izquierdo, Segismundo S. and Izquierdo, Luis R. and Gotts, Nicholas M.},
journal = {Journal of Artificial Societies and Social Simulation},
ISSN = {1460-7425},
volume = {11},
number = {2},
pages = {1},
year = {2008},
URL = {http://jasss.soc.surrey.ac.uk/11/2/1.html},
keywords = {Reinforcement Learning; Replication; Game Theory; Social Dilemmas; Agent-Based; Slow Learning},
abstract = {In this paper we replicate and advance Macy and Flache's (2002; Proc. Natl. Acad. Sci. USA, 99, 72297236) work on the dynamics of reinforcement learning in 2×2 (2-player 2-strategy) social dilemmas. In particular, we provide further insight into the solution concepts that they describe, illustrate some recent analytical results on the dynamics of their model, and discuss the robustness of such results to occasional mistakes made by players in choosing their actions (i.e. trembling hands). It is shown here that the dynamics of their model are strongly dependent on the speed at which players learn. With high learning rates the system quickly reaches its asymptotic behaviour; on the other hand, when learning rates are low, two distinctively different transient regimes can be clearly observed. It is shown that the inclusion of small quantities of randomness in players' decisions can change the dynamics of the model dramatically.},
}

The following can be copied and pasted into a text file, which can then be imported into a reference database that supports imports using the RIS format, such as Reference Manager and EndNote.


TY - JOUR
TI - Reinforcement Learning Dynamics in Social Dilemmas
AU - Izquierdo, Segismundo S.
AU - Izquierdo, Luis R.
AU - Gotts, Nicholas M.
Y1 - 2008/03/31
JO - Journal of Artificial Societies and Social Simulation
SN - 1460-7425
VL - 11
IS - 2
SP - 1
UR - http://jasss.soc.surrey.ac.uk/11/2/1.html
KW - Reinforcement Learning; Replication; Game Theory; Social Dilemmas; Agent-Based; Slow Learning
N2 - In this paper we replicate and advance Macy and Flache's (2002; Proc. Natl. Acad. Sci. USA, 99, 72297236) work on the dynamics of reinforcement learning in 2×2 (2-player 2-strategy) social dilemmas. In particular, we provide further insight into the solution concepts that they describe, illustrate some recent analytical results on the dynamics of their model, and discuss the robustness of such results to occasional mistakes made by players in choosing their actions (i.e. trembling hands). It is shown here that the dynamics of their model are strongly dependent on the speed at which players learn. With high learning rates the system quickly reaches its asymptotic behaviour; on the other hand, when learning rates are low, two distinctively different transient regimes can be clearly observed. It is shown that the inclusion of small quantities of randomness in players' decisions can change the dynamics of the model dramatically.
ER -